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ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion [article]

Andreas Hinterreiter, Peter Ruch, Holger Stitz, Martin Ennemoser, Jürgen Bernard, Hendrik Strobelt, Marc Streit
2020 arXiv   pre-print

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https://web.archive.org/web/20200721044053/https://arxiv.org/pdf/1910.00969v3.pdf

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2020 pre-print
arXiv:1910.00969v2 fatcat:otixwfarz5af7b7qfcym7naw6a
2019 pre-print
arXiv:1910.00969v1 fatcat:3iynxneihnaj3aakkv42sdo5sq
The confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind.  ...  ConfusionFlow is model-agnostic and can be used to compare performances for different model types, model architectures, and/or training and test datasets.  ...  ACKNOWLEDGMENTS This work was supported in part by the State of Upper Austria (FFG 851460, Human-Interpretable Machine Learning) and the Austrian Science Fund (FWF P27975-NBL).  ... 
arXiv:1910.00969v3 fatcat:u4f2bl45z5b7fdelbls25su2cu
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Citation

Andreas Hinterreiter, Peter Ruch, Holger Stitz, Martin Ennemoser, Jürgen Bernard, Hendrik Strobelt, Marc Streit. "ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion." arXiv (2020)